IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0242535.html
   My bibliography  Save this article

An efficient mixture of deep and machine learning models for COVID-19 diagnosis in chest X-ray images

Author

Listed:
  • Dingding Wang
  • Jiaqing Mo
  • Gang Zhou
  • Liang Xu
  • Yajun Liu

Abstract

A newly emerged coronavirus (COVID-19) seriously threatens human life and health worldwide. In coping and fighting against COVID-19, the most critical step is to effectively screen and diagnose infected patients. Among them, chest X-ray imaging technology is a valuable imaging diagnosis method. The use of computer-aided diagnosis to screen X-ray images of COVID-19 cases can provide experts with auxiliary diagnosis suggestions, which can reduce the burden of experts to a certain extent. In this study, we first used conventional transfer learning methods, using five pre-trained deep learning models, which the Xception model showed a relatively ideal effect, and the diagnostic accuracy reached 96.75%. In order to further improve the diagnostic accuracy, we propose an efficient diagnostic method that uses a combination of deep features and machine learning classification. It implements an end-to-end diagnostic model. The proposed method was tested on two datasets and performed exceptionally well on both of them. We first evaluated the model on 1102 chest X-ray images. The experimental results show that the diagnostic accuracy of Xception + SVM is as high as 99.33%. Compared with the baseline Xception model, the diagnostic accuracy is improved by 2.58%. The sensitivity, specificity and AUC of this model reached 99.27%, 99.38% and 99.32%, respectively. To further illustrate the robustness of our method, we also tested our proposed model on another dataset. Finally also achieved good results. Compared with related research, our proposed method has higher classification accuracy and efficient diagnostic performance. Overall, the proposed method substantially advances the current radiology based methodology, it can be very helpful tool for clinical practitioners and radiologists to aid them in diagnosis and follow-up of COVID-19 cases.

Suggested Citation

  • Dingding Wang & Jiaqing Mo & Gang Zhou & Liang Xu & Yajun Liu, 2020. "An efficient mixture of deep and machine learning models for COVID-19 diagnosis in chest X-ray images," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-15, November.
  • Handle: RePEc:plo:pone00:0242535
    DOI: 10.1371/journal.pone.0242535
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0242535
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0242535&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0242535?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Panwar, Harsh & Gupta, P.K. & Siddiqui, Mohammad Khubeb & Morales-Menendez, Ruben & Singh, Vaishnavi, 2020. "Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Srinka Basu & Sugata Sen, 2023. "COVID 19 Pandemic, Socio-Economic Behaviour and Infection Characteristics: An Inter-Country Predictive Study Using Deep Learning," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 645-676, February.
    2. Mustafa Abdul Salam & Sanaa Taha & Mohamed Ramadan, 2021. "COVID-19 detection using federated machine learning," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-25, June.
    3. Yan, Tao & Wong, Pak Kin & Ren, Hao & Wang, Huaqiao & Wang, Jiangtao & Li, Yang, 2020. "Automatic distinction between COVID-19 and common pneumonia using multi-scale convolutional neural network on chest CT scans," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    4. Prabhjot Kaur & Shilpi Harnal & Rajeev Tiwari & Fahd S. Alharithi & Ahmed H. Almulihi & Irene Delgado Noya & Nitin Goyal, 2021. "A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images," IJERPH, MDPI, vol. 18(22), pages 1-17, November.
    5. Toraman, Suat & Alakus, Talha Burak & Turkoglu, Ibrahim, 2020. "Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    6. Mohammad Khishe & Fabio Caraffini & Stefan Kuhn, 2021. "Evolving Deep Learning Convolutional Neural Networks for Early COVID-19 Detection in Chest X-ray Images," Mathematics, MDPI, vol. 9(9), pages 1-18, April.
    7. Panwar, Harsh & Gupta, P.K. & Siddiqui, Mohammad Khubeb & Morales-Menendez, Ruben & Bhardwaj, Prakhar & Singh, Vaishnavi, 2020. "A deep learning and grad-CAM based color visualization approach for fast detection of COVID-19 cases using chest X-ray and CT-Scan images," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    8. Jonathan S. Talahua & Jorge Buele & P. Calvopiña & José Varela-Aldás, 2021. "Facial Recognition System for People with and without Face Mask in Times of the COVID-19 Pandemic," Sustainability, MDPI, vol. 13(12), pages 1-19, June.
    9. Ben-Loghfyry, Anouar & Charkaoui, Abderrahim, 2023. "Regularized Perona & Malik model involving Caputo time-fractional derivative with application to image denoising," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    10. Tayarani N., Mohammad-H., 2021. "Applications of artificial intelligence in battling against covid-19: A literature review," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0242535. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.